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Guidance and results for the data-rich, yet information-poor.

Can You Use Predictive Analytics on Incomplete Data?

MessyDataA recent webinar attendee asked whether or not it is practical to use data mining techniques on fragmented, incomplete data. Sometimes organizations do lose data for whatever reason, and it’s not inconceivable that they would want or need to push ahead with a data mining project in spite of this lack.

The answer, as it is in so many things, is “it depends.” It depends on the business question that you are trying to answer, and it depends on how much tolerance you have for using estimates to answer those questions.

For some business questions, estimates are still going to be capable of producing a benefit. With others, estimates can and will lead you astray.

Now, there are certainly some methods which are better at tolerating noisy or incomplete data than others. And those methods can certainly be applied to your problem to produce better results.

There are also methods for estimating messy or fragmented data if necessary. Again, your project’s tolerance for estimation will have to be taken into account.

Anyone who works with your data shouldn’t be primarily concerned with the cleanliness of that data, however. There is a key question which needs to be asked instead. Can we do a better job with the data that is in front of us today?

In some cases it’s even better to have messy, consistent data than it is to have artificially cleaned data.

All of this helps to demonstrate that data analytics and predictive modeling is as much of an art as it is a science.

Given such complex considerations it is easy to see why training is the cornerstone of any good analytics project. Want to avoid pitfalls? Register for TMA’s free webinar today, or join any of TMA’s predictive analytics training courses. The Modeling Agency will equip you to make determinations about how best to handle your data.

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